Prepare for CRAN release

DESCRIPTION

@@ -1,15 +1,15 @@
 Type: Package
 Package: clustermole
 Title: Unbiased Single-Cell Transcriptomic Data Cell Type Identification
-Version: 1.0.1.9001
+Version: 1.1.0
 Authors@R: 
     person(given = "Igor",
            family = "Dolgalev",
            role = c("aut", "cre"),
            email = "[email protected]")
 Description: Assignment of cell type labels to single-cell RNA sequencing (scRNA-seq) clusters is often a time-consuming process that involves manual inspection of the cluster marker genes complemented with a detailed literature search. This is especially challenging when unexpected or poorly described populations are present. The clustermole R package provides methods to query thousands of human and mouse cell identity markers sourced from a variety of databases.
 License: MIT + file LICENSE
-URL: https://igordot.github.io/clustermole, https://github.com/igordot/clustermole
+URL: https://igordot.github.io/clustermole/, https://github.com/igordot/clustermole
 BugReports: https://github.com/igordot/clustermole/issues
 Depends: 
     R (>= 3.6)

NEWS.md

@@ -1,4 +1,4 @@
-# clustermole (development version)
+# clustermole 1.1.0
 
 * Added singscore enrichment method.
 * Added combined enrichment method.

R/enrichment.R

@@ -9,11 +9,11 @@
 #' @return A data frame of enrichment results.
 #'
 #' @references
-#' Barbie, D., Tamayo, P., Boehm, J. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. \emph{Nature} 462, 108–112 (2009). \url{https://doi.org/10.1038/nature08460}
+#' Barbie, D., Tamayo, P., Boehm, J. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. \emph{Nature} 462, 108–112 (2009). \doi{10.1038/nature08460}
 #'
-#' Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: Gene set variation analysis for microarray and RNA-Seq data. \emph{BMC Bioinformatics} 14, 7 (2013). \url{https://doi.org/10.1186/1471-2105-14-7}
+#' Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: Gene set variation analysis for microarray and RNA-Seq data. \emph{BMC Bioinformatics} 14, 7 (2013). \doi{10.1186/1471-2105-14-7}
 #'
-#' Foroutan, M., Bhuva, D.D., Lyu, R. et al. Single sample scoring of molecular phenotypes. \emph{BMC Bioinformatics} 19, 404 (2018). \url{https://doi.org/10.1186/s12859-018-2435-4}
+#' Foroutan, M., Bhuva, D.D., Lyu, R. et al. Single sample scoring of molecular phenotypes. \emph{BMC Bioinformatics} 19, 404 (2018). \doi{10.1186/s12859-018-2435-4}
 #'
 #' @import methods
 #' @import dplyr

README.md

@@ -19,7 +19,7 @@ The clustermole package provides three primary features:
 * cell type prediction based on marker genes
 * cell type prediction based on the full expression matrix
 
-Check the [documentation website](https://igordot.github.io/clustermole) for more information.
+Check the [documentation website](https://igordot.github.io/clustermole/) for more information.
 
 ---
 

cran-comments.md

@@ -1,9 +1,8 @@
 ## Test environments
 
-* local OS X install, R 3.6.1
-* travis-ci: R 3.4, R-release, R-devel
+* local OS X install, R 4.0.3
+* travis-ci: R 3.6, R-release, R-devel
 * win-builder: R-devel
-* rchk: unbuntu-rchk platform on R-hub
 
 ## R CMD check results